Paper Number
ICIS2025-1487
Paper Type
Complete
Abstract
Organizations increasingly use algorithmic systems to control worker behavior, often characterized as creating a ‘digital cage’ marked by surveillance, pressure, and dehumanization. We argue that this depiction stems from a tendency in prior research to emphasize coercive forms of algorithmic control while overlooking its enabling potential. In this study, we therefore introduce the concept of algorithmic self-control, a voluntary and intrinsically motivated engagement with algorithmic systems to achieve self-set work goals. Drawing on organizational control theory, we distinguish between coercive and enabling configurations of algorithmic control systems. We empirically investigate configurations that support algorithmic self-control through an online experiment combined with fuzzy set qualitative comparative analysis, varying goal-setting authority, data visibility, and data granularity. Our findings reveal four distinct pathways that foster algorithmic self-control in organizations. This research contributes a more balanced perspective on algorithmic control, demonstrating how such systems can empower rather than constrain workers.
Recommended Citation
Alizadeh, Armin; Doerr, Maria; Ozdemir, Furkan; and Benlian, Alexander, "From Digital Cage to Digital Compass: Configuring Algorithmic Self-Control at Work" (2025). ICIS 2025 Proceedings. 8.
https://aisel.aisnet.org/icis2025/is_transformwork/is_transformwork/8
From Digital Cage to Digital Compass: Configuring Algorithmic Self-Control at Work
Organizations increasingly use algorithmic systems to control worker behavior, often characterized as creating a ‘digital cage’ marked by surveillance, pressure, and dehumanization. We argue that this depiction stems from a tendency in prior research to emphasize coercive forms of algorithmic control while overlooking its enabling potential. In this study, we therefore introduce the concept of algorithmic self-control, a voluntary and intrinsically motivated engagement with algorithmic systems to achieve self-set work goals. Drawing on organizational control theory, we distinguish between coercive and enabling configurations of algorithmic control systems. We empirically investigate configurations that support algorithmic self-control through an online experiment combined with fuzzy set qualitative comparative analysis, varying goal-setting authority, data visibility, and data granularity. Our findings reveal four distinct pathways that foster algorithmic self-control in organizations. This research contributes a more balanced perspective on algorithmic control, demonstrating how such systems can empower rather than constrain workers.
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